System and method for detecting cancerous tissue from a thermal image

ABSTRACT

What is disclosed is a system and method for the detection of cancerous tissue by analyzing blocks of pixels in a thermal image of a region of exposed skin tissue. In one embodiment, matrices are received which have been derived from vectors of temperature values associated with pixels in blocks of pixels which have been isolated from a plurality of thermal images of both cancerous and non-cancerous tissue. The vectors are rearranged to form matrices. A thermal image of a subject is received. Blocks of pixels which reside within a region of exposed skin tissue are identified and isolated. For each identified pixel block, an image vector comprising temperature values associated with these pixels is formed. The vector is provided to a classifier which uses the matrices to classify tissue associated with this block of pixels as being either cancerous or non-cancerous tissue.

TECHNICAL FIELD

The present invention is directed to systems and methods for thedetection of cancerous tissue by analyzing blocks of pixels in a thermalimage of a region of exposed skin tissue of a patient.

BACKGROUND

Breast cancer is one of the most frequently diagnosed cancers in women.In the United States, one in eight women are likely to be diagnosed withhaving some form of breast cancer in her lifetime. Prevention throughscreening minimizes the risk of breast cancer. The effectiveness ofscreening can depend on how frequently a woman is screened. The abilityto obtain frequent screening in remote towns and villages is limited forrelatively large populations of women. The earlier the cancer can bedetected, the more likelihood that the patient responds to treatment.Accordingly, new technologies and methodologies for the detection ofcancer are increasingly needed.

BRIEF SUMMARY

What is disclosed is a system and method for the detection of canceroustissue by analyzing blocks of pixels in a thermal image of a region ofexposed skin tissue. In one embodiment, matrices are received which havebeen derived from vectors of temperature values associated with pixelsin blocks of pixels which have been isolated from a plurality of thermalimages of both cancerous and non-cancerous tissue. The vectors arerearranged to form the matrices. A thermal image of a subject isreceived and a region of exposed skin tissue is identified. Blocks ofpixels within the identified region of exposed skin tissue are isolatedfor processing. Then, for each identified block of pixels, a vector isformed from temperature values associated with pixels in this block. Thevector is then provided to a classifier. The classifier then uses thematrices to classify tissue associated with this block of pixels asbeing either cancerous or non-cancerous tissue. The method repeats forall identified blocks of pixels. In response to the detection ofcancerous tissue, an alert notification is initiated.

Features and advantages of the present system and method will becomereadily apparent from the following detailed description andaccompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other features and advantages of the subject matterdisclosed herein will be made apparent from the following detaileddescription taken in conjunction with the accompanying drawings, inwhich:

FIG. 1 shows an example thermal imaging system capturing a thermal imageof a female subject;

FIG. 2 shows one example region of exposed skin tissue which encompassesa breast area of the subject in the thermal image of FIG. 1;

FIG. 3 shows the region of exposed skin tissue of FIG. 2 wherein variousblocks of pixels of different shapes and sizes have been identified forprocessing in accordance with the teachings hereof;

FIG. 4 is a flow diagram which illustrates one example embodiment of thepresent method for detecting cancerous tissue from a thermal image;

FIG. 5 is a continuation of the flow diagram of FIG. 4 with flowprocessing continuing with respect to node A;

FIG. 6 is a continuation of the flow diagram of FIG. 6 with flowprocessing continuing with respect to node C; and

FIG. 7 is a block diagram of one example system wherein various aspectsof the present method as described with respect to the flow diagrams ofFIGS. 4-6 are implemented.

DETAILED DESCRIPTION

What is disclosed is a system and method for the detection of canceroustissue by analyzing blocks of pixels in a thermal image of a region ofexposed skin tissue.

Non-Limiting Definitions

A “subject” refers to a living being. Although the term “person” or“patient” may be used throughout this disclosure, it should beappreciated that the subject may be something other than a human suchas, for example, a primate. Therefore, the use of such terms is not tobe viewed as limiting the scope of the appended claims strictly to humansubjects.

A “thermal imaging system” is a camera with a lens that focuses infraredenergy from objects in a scene onto an array of specialized sensorswhich convert infrared energy into electrical signals on a per-pixelbasis and outputs a thermal image comprising an array of pixels withcolor values corresponding to surface temperatures of the objects in theimage across a thermal wavelength band. FIG. 1 shows a thermal imagingsystem 100 capturing a thermal image 101 of a female subject 102 which,in turn, is communicated to a workstation via a wireless transmissiveelement 103, shown as an antenna. The images of FIGS. 1-3 are forexplanatory purposes. Although the subject in FIG. 1 is female, thesubject may be male. Thermal imaging systems may incorporate a memory, astorage device, and a microprocessor capable of executing machinereadable program instructions for processing a thermal image inaccordance with the teachings hereof. Thermal imaging systems comprisingstandard equipment and those comprising hybrid devices are availablefrom vendors in various streams of commerce. The reader is directed toany of a variety of texts on thermal imaging including: “InfraredThermal Imaging: Fundamentals, Research and Applications”, MichaelVollmer, Klaus Peter Möllmann, Wiley-VCH; 1^(st) Ed. (2010) ISBN-13:978-3527407170, which is incorporated herein in its entirety byreference. In advance of processing a thermal image, a spatialresolution of the thermal image may be enhanced. A method for enhancinga resolution of an image is disclosed in: “Processing A Video ForSpatial And Temporal Magnification With Minimized Image Degradation”,U.S. patent application Ser. No. 13/708,125, by Mestha et al., which isincorporated herein in its entirety by reference. A thermal image of thesubject is received for processing.

“Receiving a thermal image” is intended to be widely construed andincludes retrieving, capturing, acquiring, or otherwise obtaining athermal image for processing in accordance with the methods disclosedherein. Thermal images can be retrieved from a memory or storage of thethermal imaging system used to acquire those images, or can be retrievedfrom a media such as a CDROM or DVD, or can be received from a remotedevice over a network. Thermal images may be downloaded from a websitewhich makes thermal images available for processing. According tovarious embodiments hereof, thermal images are analyzed to identify oneor more regions of exposed skin tissue.

A “region of exposed skin tissue” refers to an unobstructed view of asurface of the skin as seen through the aperture of the thermal cameraused to capture that thermal image. FIG. 2 shows one example region ofexposed skin tissue encompassing a breast 201 of the subject. A regionof exposed skin tissue can be identified in a thermal image using imageprocessing techniques which include, for example, object identification,pattern recognition, face detection and facial recognition, pixelclassification as well as color, texture, and spatial features. Amedical professional may manually identify regions of exposed skintissue. A region of exposed skin tissue contains one or more blocks ofpixels.

A “block of pixels” is an area of interest within a region of exposedskin tissue desired to be processed for cancer detection determinationin accordance with the methods disclosed herein. FIG. 3 shows an exampleregion of exposed skin tissue 200 wherein blocks of pixels 301, 302 and303 of various shapes and sizes have been identified for processing. Ablock of pixels can be identified using a variety of methods. In oneembodiment, a medical professional views the identified region ofexposed skin tissue and uses a mouse or a touchscreen display toidentify blocks of pixels within that region. Other methods includeobject identification, pattern recognition, face detection and facialrecognition. Pixel classification can also be utilized as well as color,texture, and spatial features. Blocks of pixels do not have to be thesame size or the same shape. As such, the shapes of the blocks of pixelsin FIG. 3 should not be viewed as limiting the scope of the appendedclaims strictly to square and rectangular shapes. Pixels may be filteredor otherwise pre-processed. Pixels may be discarded or otherwiseignored. Temperature values associated with pixels in a given block forma vector.

A “vector”, as is generally understood, has size m×1 and comprisestemperature values associated with pixels in a given block of pixels ofsize p×q, where m=pq. Methods for generating a vector are wellestablished. Vector y is used by the classifier for determining whethertissue is cancerous or non-cancerous.

“Matrices A_(H) and A_(N)” (also referred to as “learning matrices”) arederived from vectors of size m×1 rearranged to form matrices. Thevectors comprise temperature values associated with pixels in blocks ofpixels containing cancerous and non-cancerous tissues from a pluralityof thermal images. Learning matrices A_(H) and A_(N) are of size m×H andm×N, respectively, where H and N represent the number of cancerous andnon-cancerous blocks, respectively. The learning matrices may beretrieved from a memory or storage device or obtained from a remotedevice over a network. In accordance with the teachings hereof, thematrices and the formed vector are provided to a classifier.

A “classifier” is an artificial intelligence system which functions tomap a feature vector to a label that defines that feature space. Oncetrained, the classifier receives a vector for an unclassified event andclassifies that vector by assigning a label to that vector of thefeature space to which that vector belongs. Classifiers can take any ofa variety of forms including a Support Vector Machine (SVM), a neuralnetwork, a Bayesian network, a Logistic regression, Naïve Bayes,Randomized Forests, Decision Trees and Boosted Decision Trees, K-nearestneighbor, and a Restricted Boltzmann Machine (RBM), as are understood inthe machine learning arts. For an in-depth discussion, the reader isdirected to any of a wide variety of texts on classifiers, including:“Foundations of Machine Learning”, Mehryar Mohri, Afshin Rostamizadeh,Ameet Talwalkar, MIT Press (2012), ISBN-13: 978-0262018258, and “Designand Analysis of Learning Classifier Systems: A Probabilistic Approach”,Jan Drugowitsch, Springer (2008), ISBN-13: 978-3540798651, both of whichare incorporated herein in their entirety by reference. The classifieruses a compressed sensing framework to facilitate tissue classification.

The steps of “identifying”, “forming”, “obtaining”, “providing”, and“processing”, as used herein, include the application of variousmathematical operations according to any specific context or for anyspecific purpose. The terms in this disclosure and the appended claimsare intended to include any activity, in hardware or software, havingthe substantial effect of a mathematical operation. Such operative stepsmay be facilitated or otherwise effectuated by a microprocessorexecuting machine readable program instructions retrieved from a memoryor storage device.

Introduction to Compressed Sensing

A signal, f, represented as a vector of size N×1 can be expressed interms of a basis comprised of N×1 vectors. If ψ is an N×N basis matrixobtained by stacking basis vectors side by side as columns, then f canbe expressed by the following relationship:f=ψx  (1)where x is a column (coefficient) vector of size N×1 and derived fromN=L×P number of pixels, where L is the number of rows and P is thenumber of columns in the intensity matrix.

Both f and x are representations of the same signal. f is in a timedomain when this signal is a function of time. Coefficient vector x isin a basis domain ψ. f is the image vector expressed as the signal. Ifthe signal is S sparse, (i.e., the signal has at most S non-zerocomponents), then the signal is deemed to be compressible. Compressiblesignals can be approximated by S number of basis vectors. Orthogonalbasis vectors are preferred for computational reasons that one skilledin this art would readily appreciate.

If the basis matrix ψ is available then the problem of compressedsensing is to reconstruct a higher resolution vector from a lowerresolution vector. We can write a lower resolution image vector y ofsize m×1 in terms of vector f as follows:y=φf  (2)where φ is a sampling matrix of size m×N, where m<<N. It should beappreciated that the sampling matrix is a non-square sensing matrixfilled with 1's where the measurements are sampled and filled with 0'sotherwise.

Combining Eqns. (1) and (2), vector y can be rewritten as follows:y=φψx=Ax  (3)where A=φψ is a non-square sensing matrix.

Sparse coefficient vector x* can be recovered by the compressed sensingframework by using a l₁—norm minimization to solve a constrainedminimization given by:min∥x∥ _(l) ₁ such that Ax=y  (4)

x vector generated from Eqn. (4) is denoted as x*—the sparse coefficientvector. Once the sparse coefficient vector x* has been recovered, vectorf of size N×1 can be reconstructed using Eqn. (1) as follows:f*=ψx*  (5)It should be appreciated that conditions such as selecting the rightbasis matrix, sampling matrix, and number of measurement samples shouldbe properly satisfied.Classification of Tissue Using a Compressed Sensing Framework

Let A_(H) and A_(N) be matrices obtained from processing blocks ofpixels in a region of exposed skin tissue in a plurality of thermalimages of known cancerous and non-cancerous tissues, respectively. Let ybe a vector obtained from a block of pixels whose determination is to bemade. Without loss of generality, Eqn. (3) can be rewritten as:y=[A _(H) A _(N) ]x  (6)where x=[x_(H) ^(T)x_(N) ^(T)]^(T) is a coefficient vector of size(N+H)×1, x_(H) is a vector of size H×1 and x_(N) is a vector of size N×1obtained from blocks of pixels from said plurality of thermal images ofcancerous and non-cancerous tissues, respectively, and T is a transposeoperation.

In accordance with the methods disclosed herein, the classifier performsconstrained minimization using vector y and matrices A_(H) and A_(N) torecover the sparse coefficient vector x*=[(x_(H)*)^(T)(x_(N)*)^(T)]^(T)which comprises individual component sparse vectors x_(H)* and x_(N)*corresponding to cancerous pixel regions and non-cancerous pixelregions, respectively.

Once the sparse coefficient vector x* has been recovered, residues forboth cancerous and non-cancerous tissue are obtained as follows:R _(H) =∥y−A _(H) x _(H)*∥₂,  (7)R _(N) =∥y−A _(N) x _(N)*∥₂  (8)where R_(H) and R_(N) are residues of cancerous and non-canceroustissues, respectively.

If R_(H) is smaller than R_(N) then the tissue associated with the blockof pixels being tested is determined to be cancerous. Otherwise, thattissue is determined to be non-cancerous.

In another embodiment, a log of a ratio is calculated as follows:

$\begin{matrix}{\gamma = {\log\left( \frac{R_{N}}{R_{H}} \right)}} & (9)\end{matrix}$If γ>0 then the tissue associated with the block of pixels being testedis determined to be cancerous. Otherwise, that tissue is determined tobe non-cancerous.Example Flow Diagram.

Reference is now being made to the flow diagram of FIG. 4 whichillustrates one example embodiment of the present method for detectingcancerous tissue from a thermal image in accordance with the methodsdisclosed herein. Flow processing begins at step 400 and immediatelyproceeds to step 402.

At step 402, receive a set of learning matrices derived from vectors oftemperature values associated with pixels in blocks of pixels isolatedin a plurality of thermal images containing cancerous and non-canceroustissue. The vectors have been rearranged to form the matrices.

At step 404, receive a thermal image of a subject. One example thermalimage of a subject is shown in FIG. 1.

At step 406, identify regions of exposed skin tissue of the subject inthe received thermal image. An example region of exposed skin tissuewithin a thermal image is shown in FIGS. 2 and 3.

At step 408, identify blocks of pixels within each of the regions ofexposed skin tissue desired to be processed for cancer detection.Example blocks of pixels are shown in FIG. 3.

At step 410, select a first region of exposed skin tissue. The selectionis made from all the regions of exposed skin tissue which have beenidentified in step 406. Various regions may be prioritized, as needed.This selection can be made using, for example, the workstation of FIG.7.

At step 412, select a first block of pixels within the region of exposedskin tissue for. The current block of pixels to be processed is from allthe blocks of pixels which have been identified in step 408. Variousblocks of pixels may be prioritized, as needed. This selection can bemade using, for example, the workstation of FIG. 7.

Reference is now being made to the flow diagram of FIG. 5 which is acontinuation of the flow diagram of FIG. 4 with flow processingcontinuing with respect to node A.

At step 414, form a vector of temperature values associated with pixelsin the selected block of pixels.

At step 416, providing the vector and the learning matrices to aclassifier.

At step 418, a determination is made (by the classifier) whether thetissue in the selected block of pixels is cancerous. If the classifierdetermines that the tissue represented by the current block of pixels iscancerous then processing continues with respect to step 420.

At step 420, initiate an alert notification. The alert may be sent to amedical professional directly or displayed on a display device of aworkstation which reside, for instance, in a nurses station or doctor'soffice. The alert notification may comprise an audible sound whichprovides an indication that one of the pixel blocks in the thermal imageof the patient has been determined to be cancerous. Such an alert maytake the form of a canned audio message or a bell tone or a sonic alertbeing activated. Such an alert may take the form of a blinking orflashing light or a light changing from one color some other color suchas, for instance, changing from green to red. The alert may take theform of a text, audio, or video message. In this embodiment, after thealert has been initiated, processing continues with respect to step 422.In another embodiment, further processing stops when cancerous tissueshas been identified and an alert initiated.

At step 422, a determination is made whether more blocks of pixelswithin the selected region of exposed skin tissue remain to beprocessed. If so then processing continues with respect to node Bwherein, at step 408, a next block of pixels within the current regionof exposed skin tissue is selected or otherwise identified forprocessing. Processing repeats in a similar manner for each block ofpixels within the selected region of exposed skin tissue until no moreblocks of pixels remain to be processed.

Reference is now being made to the flow diagram of FIG. 6 which is acontinuation of the flow diagram of FIG. 5 with flow processingcontinuing with respect to node C.

At step 424, a determination is made whether more regions of exposedskin tissue remain to be processed. If so then processing continues withrespect to node D wherein, at step 406, a next region of exposed skintissue is selected or otherwise identified for processing. Processingrepeats for each identified block of pixels within this next selectedregion of exposed skin tissue until no more blocks of pixels remain tobe processed. If, at step 424, no more regions of exposed skin tissueremain to be processed then, in this embodiment, further processingstops.

It should be appreciated that the flow diagrams depicted herein areillustrative. One or more of the operations illustrated in the flowdiagrams may be performed in a differing order. Other operations may beadded, modified, enhanced, or consolidated. Such variations thereof areintended to fall within the scope of the appended claims.

Diagram of Image Processing System

Reference is now being made to FIG. 7 which illustrates a block diagramof one example system wherein various aspects of the present method asshown and described with respect to the flow diagrams of FIGS. 4-6 areimplemented.

In the system of FIG. 7, low cost thermal camera 701 captures a thermalimage 702 of the subject of FIG. 1. Image processing system 703 receivesthe thermal image and learning matrices 704 and 705 representingmatrices A_(H) and A_(N), respectively. System 703 is shown comprising aBuffer 706 for buffering the thermal image and the learning matrices forprocessing. The buffer utilizes storage device 707 to store/retrievevarious data, formulas, mathematical representations, and the like, asare needed to process the thermal image in accordance with the teachingshereof. Image Pre-processor 708 processes the images to compensate,where needed, for motion induced blurring, imaging blur, and otherunwanted noise and anomalies.

Region Detection Module 709 receives the pre-processed thermal image andproceeds to identify one or more regions of exposed skin tissue withinthat image. One example thermal image with one region of exposed skintissue identified thereon is shown in FIG. 2. Block Detection Module 710receives the identified regions of exposed skin tissue from Module 707and proceeds to identify one or more blocks of pixels contained thereinfor processing. Example identified blocks of pixels are shown anddiscussed with respect to FIG. 3. In one embodiment, the thermal image702 is communicated to the workstation 720 which displays the thermalimage on the display device 721 for a user to visually review. The userthen selects or otherwise identifies one or more regions of exposed skintissue and/or blocks of pixels within the identified regions forprocessing. One method for selecting regions and/or blocks of pixels isby using a mouse or a touchscreen display to draw a rubber-band boxaround regions and/or blocks of pixels within those regions. Any of theregions of exposed skin tissue and blocks of pixels may be stored tostorage device 707 using pathways not shown.

Vector Processor 711 receives the identified regions of exposed skintissue and the identified blocks of pixels within those regions andforms a vector derived from temperature values associated with pixels ineach of the identified block of pixels. The formed vector is provided toClassifier 712 which uses the vector and the matrices 704 and 705 toclassify tissue represented by the block of pixels as being eithercancerous or non-cancerous tissue.

Alert Generator Module 713 sends an alert notification signal viaantenna 714, in response to tissue being classified as cancerous. TheAlert Generator may be configured to initiate an alert notificationsignal when tissue has been determined to be non-cancerous.

The workstation has a computer case which houses a motherboard with aprocessor and memory, a network card, graphics card, and the like, andother software and hardware. The workstation reads/writes to computerreadable media 722 such as a floppy disk, optical disk, CD-ROM, DVD,magnetic tape, etc. The workstation includes a user interface which, inthis embodiment, comprises a display device 721 such as a CRT, LCD,touch screen, etc. A keyboard 723 and mouse 724 effectuate a user inputor selection.

It should be appreciated that the workstation has an operating systemand other specialized software configured to display a variety ofnumeric values, text, scroll bars, pull-down menus with user selectableoptions, and the like, for entering, selecting, or modifying informationdisplayed on display device 721. Various portions of the receivedmatrices and thermal image may be communicated to the workstation forreview, modification, and processing as needed, and stored to storagedevice 725 through pathways not shown. Although shown as a desktopcomputer, it should be appreciated that the computer workstation can beany of a laptop, mainframe, server, or a special purpose computer suchas an ASIC, circuit board, dedicated processor, or the like. Theworkstation is shown having been placed in communication with one ormore remote devices over network 726.

Some or all of the functionality performed by any of the modules andprocessing units of the image processing system 703 can be performed, inwhole or in part, by the workstation. A user may use the keyboard and/ormouse to perform or otherwise facilitate any of the functionality of anyof the modules and processing units of the image processing system 703.A user may further use the user interface of the workstation to setparameters, view images, make adjustments to the thermal imaging device,view interim results, and the like. Any of the modules and processingunits of FIG. 7 can be placed in communication with storage devices 722and 725 and may store/retrieve therefrom data, variables, records,parameters, functions, machine readable/executable program instructionsrequired to perform their intended functions. Each of the modules of theimage processing system 703 may be placed in communication with one ormore remote devices over network 726.

It should be appreciated that various modules may designate one or morecomponents which may comprise software and/or hardware designed toperform their intended functions. A plurality of modules maycollectively perform a single function. Each module may have aspecialized processor capable of executing machine readable programinstructions. A module may comprise a single piece of hardware such asan ASIC, electronic circuit, or special purpose microprocessor. Aplurality of modules may be executed by either a single special purposecomputer or a plurality of special purpose computers operating inparallel. Connections between modules include both physical and logicalconnections. Modules may further include one or more software/hardwaremodules which may further comprise an operating system, drivers, devicecontrollers, and other apparatuses some or all of which may be connectedvia a network.

Various Embodiments

One or more aspects of the present method may be implemented on adedicated computer system and may also be practiced in distributedcomputing environments where tasks are performed by remote devices thatare linked through a network. The teachings hereof can be implemented inhardware or software using any known or later developed systems,structures, devices, and/or software by those skilled in the applicableart without undue experimentation from the functional descriptionprovided herein with a general knowledge of the relevant arts.

One or more aspects of the methods described herein are intended to beincorporated in an article of manufacture, including one or morecomputer program products, having computer usable or machine readablemedia such as a floppy disk, a hard-drive, memory, CD-ROM, DVD, tape,cassette, or other digital or analog media, or the like, capable ofhaving embodied thereon a computer readable program, one or more logicalinstructions, or other machine executable codes or commands thatimplement and facilitate the function, capability, and methodologiesdescribed herein. Furthermore, the article of manufacture may beincluded on at least one storage media readable by a machinearchitecture embodying executable program instructions capable ofperforming the methodologies described in the flow diagrams. The articleof manufacture may be included as part of an operating system and may beshipped, sold, leased, or otherwise provided separately, either alone oras part of an add-on, update, upgrade, or product suite.

It will be appreciated that various of the above-disclosed and otherfeatures and functions or alternatives thereof, may be desirablycombined into many other different systems or applications. Presentlyunforeseen or unanticipated alternatives, modifications, variations, orimprovements therein may become apparent and/or subsequently made bythose skilled in the art, which are also intended to be encompassed bythe appended claims. Accordingly, the embodiments set forth herein areconsidered to be illustrative and not limiting. Various changes to theabove-described embodiments may be made without departing from thespirit and scope of the invention. The teachings of any printedpublications reference herein are each separately hereby incorporated byreference in their entirety.

What is claimed is:
 1. A method for detecting cancerous tissue from athermal image, the method comprising: receiving, with a processor,matrices A_(H) of size m×H and A_(N) of size m×N comprising vectors ofsize m×1 derived from temperature values associated with pixels inblocks of size p×q, where m=pq, which have been isolated from aplurality of thermal images of cancerous and non-cancerous tissues,where H and N represent a number of cancerous and non-cancerous blocksof pixels, respectively, said vectors being rearranged to form saidmatrices; receiving a thermal image of a subject; identifying a block ofpixels in a region of exposed skin tissue in said received thermalimage; forming a vector y of size m×1 derived from temperature valuesassociated with pixels in said identified block of pixels; and providingsaid vector to a classifier, said classifier using said matrices toclassify tissue in said thermal image represented by said block ofpixels as being one of: cancerous and a non-cancerous tissue.
 2. Themethod of claim 1, wherein vector y is represented by:y=[A _(H) A _(N) ]x where x=[x_(H) ^(T)x_(N) ^(T)]^(T) is a coefficientvector of size (N+H)×1, x_(H) is a vector of size H×1 and x_(N) is avector of size N×1 obtained from blocks of pixels from said plurality ofthermal images of cancerous and non-cancerous tissues, respectively, andT is a transpose operation.
 3. The method of claim 2, furthercomprising: performing minimization using vector y and matrices A_(H)and A_(N) to obtain a sparse coefficient vector x*, wherex*=[(x_(H)*)^(T)(x_(N)*)^(T)]^(T); and determining residues representedby:R _(N) =∥y−A _(N) x _(N)*∥₂; andR _(H) =∥y−A _(H) x _(H)*∥₂ where R_(H) and R_(N) are residues ofcancerous and non-cancerous tissues, respectively.
 4. The method ofclaim 3, wherein said minimization is a constrained minimization withconvex optimization.
 5. The method of claim 3, wherein, in response toR_(H)<R_(N), classifying tissue in said block of pixels as cancerous. 6.The method of claim 3, wherein, in response to${{\log\left( \frac{R_{N}}{R_{H}} \right)} > 0},$ classifying tissue insaid block of pixels as cancerous.
 7. The method of claim 1, whereinsaid classifier is any of: Support Vector Machine (SVM), a neuralnetwork, a Bayesian network, a Logistic regression, Naïve Bayes,Randomized Forests, Decision Trees, Boosted Decision Trees, K-nearestneighbor, Restricted Boltzmann Machine (RBM), and a hybrid systemcomprising any combination hereof.
 8. The method of claim 1, wherein, inadvance of identifying a block of pixels in said thermal image forprocessing, further comprising enhancing a spatial resolution of saidthermal image.
 9. The method of claim 1, wherein said matrices A_(H) andA_(N) are one of; basis matrices, product of sampling matrices and basismatrices, dictionary matrices, and random matrices.
 10. The method claim1, wherein said vector y comprises temperature values containing anumber of rows which is less than a number of rows in matrices A_(H) andA_(N).
 11. The method of claim 1, wherein, in response to said subjectbeing classified as having cancerous tissue, performing any of:initiating an alert, and signaling a medical professional.
 12. Themethod of claim 1, further comprising communicating said subject'sclassification to any of: a memory, a storage device, a display device,a handheld wireless device, a handheld cellular device, and a remotedevice over a network.
 13. A system for detecting cancerous tissue froma thermal image, the system comprising: a classifier; and a processor incommunication with a memory and said classifier, said processorexecuting machine readable instructions for performing: receivingmatrices A_(H) of size m×H and A_(N) of size m×N comprising vectors ofsize m×1 derived from temperature values associated with pixels inblocks of size p×q, where m=pq, which have been isolated from aplurality of thermal images of cancerous and non-cancerous tissues,where H and N represent a number of cancerous and non-cancerous blocksof pixels, respectively, said vectors being rearranged to form saidmatrices; receiving a thermal image of a subject; identifying a block ofpixels in a region of exposed skin tissue in said received thermalimage; forming a vector y of size m×1 derived from temperature valuesassociated with pixels in said identified block of pixels; and providingsaid vector to said classifier, said classifier using said matrices toclassify tissue in said thermal image represented by said block ofpixels as being one of: cancerous and a non-cancerous tissue.
 14. Thesystem of claim 13, wherein said vector y is represented by:y=[A _(H) A _(N) ]x where x=[x_(H) ^(T)x_(N) ^(T)]^(T) is a coefficientvector of size (N+H)×1, x_(H) is a vector of size H×1 and x_(N) is avector of size N×1 obtained from blocks of pixels from said plurality ofthermal images of cancerous and non-cancerous tissues, respectively, andT is a transpose operation.
 15. The system of claim 14, furthercomprising: performing minimization using vector y and matrices A_(H)and A_(N) to obtain a sparse coefficient vector x*, wherex*=[(x_(H)*)^(T)(x_(N)*)^(T)]^(T); and determining residues representedby:R _(N) =∥y−A _(N) x _(N)*∥₂; andR _(H) =∥y−A _(H) x _(H)*∥₂ where R_(H) and R_(N) are residues ofcancerous and non-cancerous tissues, respectively.
 16. The system ofclaim 15, wherein said minimization is a constrained minimization withconvex optimization.
 17. The system of claim 15, wherein, in response toR_(H)<R_(N), classifying tissue in said block of pixels as cancerous.18. The system of claim 15, wherein, in response to${{\log\left( \frac{R_{N}}{R_{H}} \right)} > 0},$ classifying tissue insaid block of pixels as cancerous.
 19. The system of claim 13, whereinsaid classifier is any of: Support Vector Machine (SVM), a neuralnetwork, a Bayesian network, a Logistic regression, Naïve Bayes,Randomized Forests, Decision Trees, Boosted Decision Trees, K-nearestneighbor, Restricted Boltzmann Machine (RBM), and a hybrid systemcomprising any combination hereof.
 20. The system of claim 13, wherein,in advance of identifying a block of pixels in said thermal image forprocessing, further comprising enhancing a spatial resolution of saidthermal image.
 21. The system of claim 13, wherein said matrices A_(H)and A_(N) are one of; basis matrices, product of sampling matrices andbasis matrices, dictionary matrices, and random matrices.
 22. The systemof claim 13, wherein said vector y comprises temperature valuescontaining a number of rows which is less than a number of rows inmatrices A_(H) and A_(N).
 23. The system of claim 13, wherein, inresponse to said subject being classified as having cancerous tissue,performing any of: initiating an alert, and signaling a medicalprofessional.
 24. The system of claim 13, further comprisingcommunicating said subject's classification to any of: a memory, astorage device, a display device, a handheld wireless device, a handheldcellular device, and a remote device over a network.
 25. The system ofclaim 13, wherein said classification occurs in real-time.